# In[61]:
pop = toolbox.population(n=n_pop) #
hof = tools.HallOfFame(
champs
) # only record the best three individuals ever found in all generations
startDT = datetime.datetime.now()
print(str(startDT))
# start evolution
pop, log = gep.gep_simple(pop,
toolbox,
n_generations=n_gen,
n_elites=1,
stats=stats,
hall_of_fame=hof,
verbose=True)
print("Evolution times were:\n\nStarted:\t", startDT, "\nEnded: \t",
str(datetime.datetime.now()))
print(hof[0])
best_ind = hof[0]
print(best_ind)
symplified_best = gep.simplify(best_ind)
print(symplified_best)
def search_model():
# define fitness and individual type
creator.create('FitnessMax', base.Fitness, weights=(1,))
creator.create('Individual', Chromosome, fitness=creator.FitnessMax)
# create individuals (genotype space)
toolbox = Toolbox()
toolbox.register('gene', Gene, pset, args.head_length)
toolbox.register('individual', creator.Individual, toolbox.gene, args.num_genes)
toolbox.register('population', tools.initRepeat, list, toolbox.individual)
# translate individuals into computation graph (phenotype space)
toolbox.register('comp_graph', cell_graph.generate_comp_graph)
# evaluation function
def evaluate(indv):
_, comp_graph = toolbox.comp_graph(indv)
net = build_model(comp_graph)
acc = train_model(net)
#size = count_parameters(net)
fit = acc[1].item() #+ 1 / expit(size)
return fit,
# evaluate and select
toolbox.register('evaluate', evaluate)
toolbox.register('select', tools.selRoulette)
# recombine and mutate
#toolbox.register('cx_1p', cross_one_point, pb=args.cx_pb)
toolbox.register('cx_gene', cross_gene, pb=args.cx_pb[0])
toolbox.register('cx_2p', cross_two_point, pb=args.cx_pb[1])
toolbox.register('mut_uniform', mutate_uniform, pset=pset, pb=args.mu_pb)
toolbox.register('mut_invert_program', invert_program, pb=args.invert_pb)
#toolbox.register('mut_invert_cell', invert_cell, pb=args.invert_pb)
toolbox.register('mut_transpose_program', transpose_program, pb=args.transpose_pb)
toolbox.register('mut_transpose_cell', transpose_cell, pb=args.transpose_pb)
# evolution statistics
stats = tools.Statistics(key=lambda ind: ind.fitness.values[0])
stats.register("avg", np.mean)
stats.register("std", np.std)
stats.register("min", np.min)
stats.register("max", np.max)
# population size and best individual (hall of fame)
pop = toolbox.population(n=args.pop_size)
hof = tools.HallOfFame(args.hof)
# call gep_simple evolutionary algorithm
pop, log = gep_simple(pop,
toolbox,
n_generations=args.num_gen,
n_elites=args.elites,
stats=stats,
hall_of_fame=hof,
verbose=True)
print('\n', log)
# save and draw best individuals graphs
for i, best in enumerate(hof):
graph, _ = cell_graph.generate_comp_graph(best)
cell_graph.save_graph(graph, args.path+'/best/indv_{}'.format(i))
cell_graph.draw_graph(graph, args.path+'/best/indv_{}'.format(i))
# save population graphs
for i, p in enumerate(pop):
graph, _ = cell_graph.generate_comp_graph(p)
cell_graph.save_graph(graph, args.path+'/pop/indv_{}'.format(i))
# save stats record
with open(args.path+'/best/stats.pkl', 'wb') as f:
pickle.dump(log, f,)
